1. Introduction
The carbon sequestration rate (CSR) is an important indicator for measuring the ecosystem carbon sink function [
1], which has attracted increasing attention, particularly in the context of intensifying global climate change. It is estimated that global terrestrial ecosystems sequester approximately 2.4 billion tons of carbon per year, absorbing around 30% of the CO₂ emissions caused by human activities [
2], thus playing a crucial role in combating climate change and maintaining ecosystem health. However, the differences in natural geographic conditions and human activities lead to significant spatial heterogeneity in the regional CSR, particularly in Ningxia, a semi-arid region with a fragile ecological environment, low vegetation cover, and a high susceptibility to climate conditions and human activities [
3]. Therefore, CSR estimates based on fixed values are unable to accurately reflect the carbon sequestration status in different regions, thereby increasing the uncertainty of the results.
In recent years, with the implementation of a series of ecological restoration projects, vegetation conditions in Ningxia have improved; however, systematic research on the spatiotemporal variation characteristics of its CSR is still lacking [
4]. Therefore, an in-depth study of the CSR in the Ningxia region will provide scientific support for more accurate carbon sequestration accounting and the implementation of ecological protection and restoration projects. Currently, methods for estimating CSR mainly include traditional approaches based on ground-based measurements and process models or atmospheric inversion methods based on remote sensing data [
5]. Traditional methods primarily rely on long-term observational data from ground-based sampling points. While they can provide relatively accurate CSR information, their application on large scales is limited by the number and spatial distribution of the sampling points [
6]. With technological advancements, methods such as the eddy covariance technique [
7] and ecosystem process models (e.g., CENTURY and Biome-BGC models) [
8] have gradually become important tools for assessing the CSR. In recent years, the application of remote sensing technology has provided new perspectives for the large-scale monitoring and assessment of ecosystem carbon stocks [
9,
10]. For example, Feng et al. (2013) used remote sensing technology to quantitatively assess carbon sink changes from 2000 to 2008 in the Grain for Green project on the Loess Plateau, finding that the total carbon sink in the region was approximately 96.1 Tg [
11].
Significant progress has been made in CSR research both domestically and internationally. For example, Lin demonstrated that forests have a higher CSR potential due to their rich biomass [
12]. Mitsch, by comparing the CSR of different types of wetlands (e.g., marshes, peatlands, and mangroves), concluded that wetlands, particularly peatlands, have a high carbon sequestration capacity [
13]. Lal and Aubrey, among others, have demonstrated the importance of soil carbon sequestration in agricultural lands for global climate change and food security [
14,
15]. Building on this, CSR research has gradually focused on the synergistic effects of various influencing factors. For instance, Mekonnen et al. demonstrated that climate change is a key factor affecting the CSR [
16]. Moisa et al. indicated that human activities, such as land use changes and urbanization, significantly impact the CSR, with large-scale land reclamation and vegetation destruction reducing the carbon storage capacity of ecosystems [
17]. Topographic factors (elevation, slope, and aspect), soil factors (the soil organic matter content, fertility, and pH), and vegetation types have all been shown to significantly affect the CSR [
18,
19,
20]. Bu et al.’s study indicated that from 2000 to 2015, Ningxia’s wetland restoration project led to an increase of 204,900 tons of carbon storage [
2]. Although previous studies have provided some theoretical support for Ningxia’s carbon sequestration capacity, most existing research focuses on the effects of single ecosystem types or specific factors, lacking a spatiotemporal dynamic analysis of the CSR across the entire terrestrial ecosystem at the regional scale under the influence of multiple factors. Research has mainly focused on regional-scale studies, with less attention given to the spatial heterogeneity of the CSR within the region, and limited studies on the current CSR status in Ningxia.
Therefore, this study proposes to utilize extensive ground-based data, combined with long-term remote sensing data, topographic and soil data, and machine learning techniques to generate vegetation and soil carbon density datasets for the period 2000–2023. The dataset will be further used to derive a 30 m high-resolution CSR dataset for 2001–2023, which will be analyzed for spatial heterogeneity and dynamic characteristics across different ecological regions of Ningxia. This will reveal the evolution of the CSR in Ningxia and provide scientific support for achieving the region’s dual carbon goals. At the same time, this study will assess the impact of different land use types on the regional CSR, providing decision-making references for optimizing the land use structure and the rational layout of vegetation restoration projects. This will contribute to the promotion of ecological environment construction in Ningxia and support the achievement of carbon neutrality goals, offering practical value and scientific support for regional sustainable development.
5. Conclusions
This study, based on ground observation data and multimodal datasets, employs the EXT optimal machine learning model to invert a 30m resolution VTCD and SOCD dataset for Ningxia from 2000 to 2023. It further evaluates the spatiotemporal distribution characteristics of carbon sequestration rates (CSR) from 2001 to 2023 and reveals the associated influencing factors. The results indicate that:
- (1)
During 2001–2023, the CSR of Ningxia’s ecosystems exhibited a spatial distribution characterized by higher values in the south and lower values in the north. The mean CSR was 21.95 gC·m⁻2, with an overall fluctuating upward trend and a growth rate of 0.53 gC·m⁻2·a⁻1.
- (2)
The CSR means significantly differ across different ecological regions. The Liupan Mountain water erosion area had the highest carbon sequestration capacity with a mean of 46.51 gC·m⁻2, while the Helan Mountain water erosion zone had the lowest CSR mean of 11.34 gC·m⁻2. The carbon sequestration rate in the Water Erosion Area of Loess Hilly and Gully Residual Tableland showed the most significant increase, with an annual growth rate of 1.16 gC·m⁻2·a⁻1.
- (3)
For land use types with unchanged coverage, the carbon sequestration capacity is ranked as forest > cropland > grassland > barren, while the enhancement capacity is ranked as cropland > forest > grassland > barren. In terms of land-use change types, the CSR ranking is as follows: G-F > C-Fg > B-Fg. The enhancement capacity ranking is C-Fg > G-F > B-Fg.
Compared to grassland, cropland, and barren land, the transitions of G-F, C-Fg, and B-Fg can enhance carbon sequestration capacity by 42.9%, 9.2%, and 34.6%, respectively.
- (4)
Among the individual influencing factors, the NDVI is the primary driver of the spatiotemporal dynamics of the CSR in Ningxia’s ecosystems. However, the two-factor interaction between the EVI and Bulk Density provides a more significant explanatory power for the CSR.
This study demonstrates that ecological restoration projects such as returning farmland to forest (grassland) and conservation tillage play a significant role in enhancing the regional carbon sequestration capacity. Future carbon-neutral policies for Ningxia should prioritize the implementation of vegetation restoration measures and further optimize the design and management of restoration projects across different ecological regions to maximize their carbon sequestration benefits.
